Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair
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Abstract (arXiv)
All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendly to ordinary people. To achieve point-and-shoot AIF photography with a smartphone, we expect that an AIF photo can be generated from one shot of the scene, instead of from multiple photos captured by the same camera. Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras. The goal is to recover sharp details from defocused regions in the main-camera photo with the help of the ultra-wide-camera one. The camera setting poses new challenges such as parallax-induced occlusions and inconsistent color between cameras. To overcome the challenges, we introduce a predict-and-refine network to mitigate occlusions and propose dynamic frequency-domain alignment for color correction. To enable effective training and evaluation, we also build an AIF dataset with 2686 unique scenes. Each scene includes two photos captured by the main camera, one photo captured by the ultrawide camera, and a synthesized AIF photo. Results show that our solution, termed EasyAIF, can produce high-quality AIF photos and outperforms strong baselines quantitatively and qualitatively. For the first time, we demonstrate point-and-shoot AIF photo synthesis successfully from main and ultra-wide cameras.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 61 references that are in the map. First posted on arXiv 11 Apr 2023.
Builds on
26 of 61 references.
References · 26
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14 more
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Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2023Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Trainingcited by 2
- 2025Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detectioncited by 2
- 2026LensStyle: Learning the Optical Aesthetics for Controllable Stylized Lens Effect Renderingcited by 1
- 2025Dual-Camera All-in-Focus Neural Radiance Fieldscited by 0
- 2026LensStyle: Learning the Optical Aesthetics for Controllable Stylized Lens Effect Rendering4 Sep 2026
- 2025Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detection27 Sep 2025
- 2025Dual-Camera All-in-Focus Neural Radiance Fields23 Apr 2025
- 2023Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training15 Aug 2023